Please feel free to contribute to the Github repo here. You can start by cloning this repository or contacting me at my email to be added as a collaborator. Could certainly use your help!
knitr::opts_chunk$set(
error = FALSE,
message = FALSE,
warning = FALSE
)
library(tidyverse)
library(rvest)
library(magrittr)
library(lubridate)
library(plotly)
Understanding our data sources
John Hopkins CSSE
Daily confirmed cases, deaths and recoveries broken down by country/region and state/province, when state/province is available.
confirmed_ts <- read_csv(url("https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_19-covid-Confirmed.csv")) %>%
gather(date, confirmed, -`Province/State`, -`Country/Region`, -Lat, -Long) %>%
mutate(date = mdy(date))
deaths_ts <- read_csv(url("https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_19-covid-Deaths.csv")) %>%
gather(date, deaths, -`Province/State`, -`Country/Region`, -Lat, -Long) %>%
mutate(date = mdy(date))
recovered_ts <- read_csv(url("https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_19-covid-Recovered.csv")) %>%
gather(date, recovered, -`Province/State`, -`Country/Region`, -Lat, -Long) %>%
mutate(date = mdy(date))
joined <- confirmed_ts %>%
left_join(deaths_ts) %>%
left_join(recovered_ts) %>%
gather(stat, value, confirmed, deaths, recovered) %>%
arrange(`Country/Region`, `Province/State`, date, stat)
joined %>% mutate_if(is.character, as.factor) %>% summary()
Province/State Country/Region Lat Long date stat
Diamond Princess: 330 US :40755 Min. :-41.45 Min. :-157.86 Min. :2020-01-22 confirmed:25465
Grand Princess : 330 China : 5445 1st Qu.: 27.61 1st Qu.: -93.06 1st Qu.:2020-02-04 deaths :25465
Adams, IN : 165 Canada : 1815 Median : 37.81 Median : -74.30 Median :2020-02-18 recovered:25465
Alabama : 165 Australia : 1485 Mean : 31.81 Mean : -36.13 Mean :2020-02-18
Alachua, FL : 165 France : 1155 3rd Qu.: 42.33 3rd Qu.: 20.94 3rd Qu.:2020-03-03
(Other) :50985 United Kingdom: 660 Max. : 71.71 Max. : 174.89 Max. :2020-03-16
NA's :24255 (Other) :25080
value
Min. : 0.0
1st Qu.: 0.0
Median : 0.0
Mean : 66.8
3rd Qu.: 0.0
Max. :67798.0
#' Prepares data.frame for plotting by aggregating, weighting, reordering,
#' and filtering by location.
#'
#' @param geo_level column name to aggregate (sum) by
#' @param dcr_wts weights to order locations; vector of c(deaths, confirmed, recovered)
#' @param show_top_n show top n locations ordered by dcr_wts
#'
#' @return an aggregated, reordered, and filtered by location data.frame
#'
#' @examples
#' plot_prep("Country/Region", c(100, 1, -1), show_top_n = 25)
#'
#' @export
plot_prep <- function(df, geo_level = "Country/Region", dcr_wts = c(1, 0.01, 0),
show_top = 1:10) {
df %>%
mutate(location = !!sym(geo_level)) %>%
# aggregate by location
group_by(location, date, stat) %>%
summarise(value = sum(value)) %>%
ungroup() %>%
# location order
group_by(location) %>%
mutate(
total_wt =
value[which(date == max(date) & stat == "deaths")] * dcr_wts[1] +
value[which(date == max(date) & stat == "confirmed")] * dcr_wts[2] +
value[which(date == max(date) & stat == "recovered")] * dcr_wts[3]
) %>%
ungroup() %>%
mutate(total_rank = dense_rank(desc(total_wt))) %>%
# filter top n
filter(total_rank %in% show_top) %>%
# order by rank
mutate(location = fct_reorder(location, total_rank))
}
Cross-country comparison
#' Convenience function to plot country comparisons
plot_country_comps <- function(df) {
df %>%
ggplot(aes(date, value, color=location)) +
geom_line() + facet_wrap(vars(stat), ncol=1, scales = "free_y") +
theme(legend.position = "right", legend.title = element_blank()) +
xlab(element_blank())
}
Top countries
joined %>%
plot_prep("Country/Region") %>%
plot_country_comps()

excl China
joined %>%
filter(!`Country/Region` %in% c("China")) %>%
plot_prep("Country/Region") %>%
plot_country_comps()

excl Italy, Iran, South Korea
joined %>%
filter(!`Country/Region` %in% c("China", "Italy", "Iran", "Korea, South")) %>%
plot_prep("Country/Region") %>%
plot_country_comps()

excl Spain, France
joined %>%
filter(!`Country/Region` %in% c("China", "Italy", "Iran", "Korea, South",
"Spain", "France")) %>%
plot_prep("Country/Region") %>%
plot_country_comps()

Comparing growth
#' Convenience function to plot growth comparisons
plot_growth_comps <- function(df) {
df %>%
ggplot(aes(date, value)) + geom_line() +
theme(axis.text.x = element_text(angle = 45, hjust = 1, vjust = 1)) +
facet_wrap(
vars(location, stat), scales="free_y", ncol = 3,
labeller=label_wrap_gen(multi_line=FALSE)) +
theme(strip.background = element_blank(), legend.position = "None") +
xlab(element_blank())
}
By country
joined %>%
plot_prep() %>%
plot_growth_comps()

China
joined %>%
filter(`Country/Region` == "China") %>%
plot_prep("Province/State") %>%
plot_growth_comps()

US
joined %>%
filter(`Country/Region` == "US") %>%
plot_prep("Province/State") %>%
plot_growth_comps()

Canada
joined %>%
filter(`Country/Region` == "Canada") %>%
plot_prep("Province/State") %>%
plot_growth_comps()

Australia
joined %>%
filter(`Country/Region` == "Australia") %>%
plot_prep("Province/State") %>%
plot_growth_comps()

Total deaths since first death
plot_deaths_since_first <- function(df, days_since, init_deaths = 3,
show_top = 1:10) {
xlab <- paste("Days since deaths =", init_deaths)
df %>%
plot_prep("Country/Region", show_top = show_top) %>%
group_by(location) %>%
mutate(total_deaths = value[which(date == max(date) &
stat == "deaths")][1]) %>%
filter(total_deaths > 0) %>%
mutate(init_death_date = date[which(value >= init_deaths &
stat == "deaths")][1]) %>%
mutate(
!!sym(xlab) := date - init_death_date) %>%
filter(stat == "deaths" & value > 0 &
between(!!sym(xlab), 0, days_since)) %>%
ggplot(aes(!!sym(xlab), value,
color=location)) + geom_line() +
theme(strip.background = element_blank(), legend.position = "right",
legend.title = element_blank()) +
ylab("Total deaths")
}
Dashboard version
10 days since first death
joined %>%
plot_deaths_since_first(10)

30 days since first death
joined %>%
plot_deaths_since_first(30)

60 days since first death
joined %>%
plot_deaths_since_first(60)

Double days
Double days by country/stat
Top 10 countries
joined %>%
plot_prep(dcr_wts = c(100, 0.1, 0), show_top = 1:10) %>%
group_by(location, stat) %>%
arrange(date) %>%
mutate(
`Days to double` = if_else(
value < 10, as.difftime("", units="days"),
date - date[sapply(value, function(x) { which.min(abs(x / 2 - value)) })])
) %>%
ggplot(aes(date, `Days to double`, color=stat)) +
geom_point(alpha = 0.5) +
geom_smooth() +
#facet_wrap(vars(location), ncol = 3) +
facet_grid(vars(location), vars(stat), scales="free") +
theme(legend.position = "bottom", legend.title=element_blank()) +
xlab(element_blank())

excl China
joined %>%
filter(`Country/Region` != "China") %>%
plot_prep(dcr_wts = c(100, 0.1, 0), show_top = 1:10) %>%
group_by(location, stat) %>%
arrange(date) %>%
mutate(
`Days to double` = if_else(
value < 10, as.difftime("", units="days"),
date - date[sapply(value, function(x) { which.min(abs(x / 2 - value)) })])
) %>%
ggplot(aes(date, `Days to double`, color=stat)) +
geom_point(alpha = 0.5) +
geom_smooth() +
#facet_wrap(vars(location), ncol = 3) +
facet_grid(vars(location), vars(stat), scales="free") +
theme(legend.position = "bottom", legend.title=element_blank()) +
xlab(element_blank())

Days to double deaths since nth death
h/t: @loeserjohn
Dashboard version
Country/Region level
n_deaths <- 10
min_deaths <- n_deaths * 2
top_n_countries <- 1:20
trunc_to_n <- 2
geo_level = "Country/Region"
# TODO(roboton): Refactor into prep and plot functions
joined %>%
# plot prep at country level
plot_prep(geo_level, show_top = top_n_countries) %>%
# limit to deaths
filter(stat == "deaths") %>%
# drop countries with less than min_deaths
group_by(location) %>%
filter(value[which.max(date)] >= min_deaths) %>%
ungroup() %>%
# get the first date with more than or equal to n_deaths
group_by(location) %>%
mutate(nth_death_date = date[which(value >= n_deaths) - 1][1]) %>%
mutate(`Days since nth death` = date - nth_death_date) %>%
# Drop days before nth deaths date
filter(`Days since nth death` > 0) %>%
# Compute Days to Double
group_by(location) %>%
mutate(
# set days to double
double_idx = sapply(value, FUN=function(x) { max(which(value <= x/2)) }),
`Days to double` = date - date[double_idx]
) %>%
filter(!is.na(`Days to double`)) %>%
## plot
# truncate to second longest time series
group_by(location) %>%
mutate(max_days_since_nth_death = max(`Days since nth death`)) %>%
ungroup() %>%
mutate(max_days_rank = dense_rank(desc(max_days_since_nth_death))) %>%
filter(`Days since nth death` <= max(`Days since nth death`[max_days_rank == trunc_to_n])) %>%
group_by(location) %>%
mutate(time_points = n()) %>%
ungroup() %>%
filter(time_points > 2) %>%
ggplot(aes(`Days since nth death`, `Days to double`, color=location)) +
geom_line(stat = "smooth", method = "auto", se = F) +
geom_point(alpha = 0.2, size = 1) +
xlab(paste("days since deaths =", n_deaths)) -> p
ggplotly(p)
Long view
n_deaths <- 10
min_deaths <- n_deaths * 2
top_n_countries <- 1:15
trunc_to_n <- 1
geo_level = "Country/Region"
# TODO(roboton): Refactor into prep and plot functions
joined %>%
#filter(!is.na(`Province/State`) & `Province/State` != `Country/Region`) %>%
# plot prep at country level
plot_prep(geo_level, show_top = top_n_countries) %>%
# limit to deaths
filter(stat == "deaths") %>%
# drop countries with less than min_deaths
group_by(location) %>%
filter(value[which.max(date)] >= min_deaths) %>%
ungroup() %>%
# get the first date with more than or equal to n_deaths
group_by(location) %>%
mutate(nth_death_date = date[which(value >= n_deaths) - 1][1]) %>%
mutate(`Days since nth death` = date - nth_death_date) %>%
# Drop days before nth deaths date
filter(`Days since nth death` > 0) %>%
# Compute Days to Double
group_by(location) %>%
mutate(
# set days to double
double_idx = sapply(value, FUN = function(x) { max(which(value <= x/2)) }),
`Days to double` = date - date[double_idx]
) %>%
filter(!is.na(`Days to double`)) %>%
## plot
# truncate to second longest time series
group_by(location) %>%
mutate(max_days_since_nth_death = max(`Days since nth death`)) %>%
ungroup() %>%
mutate(max_days_rank = dense_rank(desc(max_days_since_nth_death))) %>%
filter(`Days since nth death` <= max(`Days since nth death`[max_days_rank == trunc_to_n])) %>%
group_by(location) %>%
mutate(time_points = n()) %>%
ungroup() %>%
filter(time_points > 2) %>%
ggplot(aes(`Days since nth death`, `Days to double`,
color = location)) +
geom_line(stat = "smooth", method = "auto", se = F) +
geom_point(alpha = 0.2, size = 1) +
xlab(paste("days since deaths >=", n_deaths)) -> p
ggplotly(p)
Prototype
min_total <- 10
min_stat <- "deaths"
geo_level <- "Country/Region"
max_days_since <- 20
min_total <- 50
joined %>%
rename(total = value) %>%
mutate(location = !!sym(geo_level)) %>%
# aggregate
group_by(location, stat, date) %>%
summarise(total = sum(total)) %>%
# add cfr
bind_rows(
(.) %>%
select(location, stat, date, total) %>%
spread(stat, total) %>%
mutate(cfr = deaths/confirmed) %>%
gather(stat, total, confirmed, deaths, recovered, cfr) %>%
filter(stat == "cfr")) %>%
# get max_total and first_date per location/stat
group_by(location, stat) %>%
mutate(max_total = max(total),
first_date = min(date[total >= min_total])) %>%
group_by(location) %>%
# drop earlier dates
filter(date >= first_date[stat == min_stat]) %>%
# recenter dates
mutate(days_since = date - first_date[stat == min_stat]) %>%
ungroup() %>%
# calc double_days
group_by(location, stat) %>%
mutate(
double_days = date -
date[sapply(
total, FUN = function(x) { max(which(total <= x/2)) })]) %>%
ungroup() %>%
gather(value_type, value, total, double_days) %>%
## plotting
# truncate days_since
filter(days_since <= max_days_since) %>%
# dropping small locations
group_by(location) %>%
filter(max_total[stat == min_stat][1] >= min_total) %>%
ungroup() %>%
ggplot(aes(days_since, value, color = location)) +
geom_line() +
facet_wrap(vars(stat, value_type), scales = "free", ncol=2,
labeller = label_wrap_gen(multi_line = FALSE))

---
title: "COVID-19 Exploratory"
output:
  html_notebook:
    code_folding: hide
    theme: lumen
    toc: true
    toc_float: true
---

Please feel free to contribute to the [Github repo](https://github.com/roboton/covid-19_meta) here.  You can start by [cloning](https://help.github.com/en/github/creating-cloning-and-archiving-repositories/cloning-a-repository) this repository or contacting me at my [email](mailto:roberton@gmail.com) to be added as a collaborator.  Could certainly use your help!

```{r setup}
knitr::opts_chunk$set(
	error = FALSE,
	message = FALSE,
	warning = FALSE
)
library(tidyverse)
library(rvest)
library(magrittr)
library(lubridate)
library(plotly)
```

# Understanding our data sources

## John Hopkins CSSE

Daily confirmed cases, deaths and recoveries broken down by country/region and state/province, when state/province is available.

```{r}
confirmed_ts <- read_csv(url("https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_19-covid-Confirmed.csv")) %>%
  gather(date, confirmed, -`Province/State`, -`Country/Region`, -Lat, -Long) %>%
  mutate(date = mdy(date))
deaths_ts <- read_csv(url("https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_19-covid-Deaths.csv")) %>%
  gather(date, deaths, -`Province/State`, -`Country/Region`, -Lat, -Long) %>%
  mutate(date = mdy(date))
recovered_ts <- read_csv(url("https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_19-covid-Recovered.csv")) %>%
  gather(date, recovered, -`Province/State`, -`Country/Region`, -Lat, -Long) %>%
  mutate(date = mdy(date))

joined <- confirmed_ts %>%
  left_join(deaths_ts) %>%
  left_join(recovered_ts) %>%
  gather(stat, value, confirmed, deaths, recovered) %>%
  arrange(`Country/Region`, `Province/State`, date, stat)

joined %>% mutate_if(is.character, as.factor) %>% summary()
```
```{r}
#' Prepares data.frame for plotting by aggregating, weighting, reordering, 
#' and filtering by location.
#'
#' @param geo_level column name to aggregate (sum) by
#' @param dcr_wts weights to order locations; vector of c(deaths, confirmed, recovered)
#' @param show_top_n show top n locations ordered by dcr_wts
#'
#' @return an aggregated, reordered, and filtered by location data.frame
#'
#' @examples
#' plot_prep("Country/Region", c(100, 1, -1), show_top_n = 25)
#'
#' @export
plot_prep <- function(df, geo_level = "Country/Region", dcr_wts = c(1, 0.01, 0),
                      show_top = 1:10) {
  df %>%
    mutate(location = !!sym(geo_level)) %>%
    # aggregate by location
    group_by(location, date, stat) %>%
    summarise(value = sum(value)) %>%
    ungroup() %>%
    # location order
    group_by(location) %>%
    mutate(
      total_wt =
        value[which(date == max(date) & stat == "deaths")] * dcr_wts[1] +
        value[which(date == max(date) & stat == "confirmed")] * dcr_wts[2] +
        value[which(date == max(date) & stat == "recovered")] * dcr_wts[3]
    ) %>%
    ungroup() %>%
    mutate(total_rank = dense_rank(desc(total_wt))) %>%
    # filter top n
    filter(total_rank %in% show_top) %>%
    # order by rank
    mutate(location = fct_reorder(location, total_rank)) 
}
```

# Cross-country comparison {.tabset .tabset-fade .tabset-pills}

```{r}
#' Convenience function to plot country comparisons
plot_country_comps <- function(df) {
  df %>% 
    ggplot(aes(date, value, color=location)) +
    geom_line() + facet_wrap(vars(stat), ncol=1, scales = "free_y") +
    theme(legend.position = "right", legend.title = element_blank()) +
    xlab(element_blank())
}
```


## Top countries

```{r fig.height = 8}
joined %>%
  plot_prep("Country/Region") %>%
  plot_country_comps()
```

## excl China

```{r fig.height = 8}
joined %>%
  filter(!`Country/Region` %in%  c("China")) %>%
  plot_prep("Country/Region") %>%
  plot_country_comps()
```

## excl Italy, Iran, South Korea

```{r fig.height = 8}
joined %>%
  filter(!`Country/Region` %in%  c("China", "Italy", "Iran", "Korea, South")) %>%
  plot_prep("Country/Region") %>%
  plot_country_comps()
```

## excl Spain, France

```{r fig.height = 8}
joined %>%
  filter(!`Country/Region` %in%  c("China", "Italy", "Iran", "Korea, South",
                                   "Spain", "France")) %>%
  plot_prep("Country/Region") %>%
  plot_country_comps()
```

# Comparing growth {.tabset .tabset-fade .tabset-pills}

```{r}
#' Convenience function to plot growth comparisons
plot_growth_comps <- function(df) {
  df %>%
    ggplot(aes(date, value)) + geom_line() +
    theme(axis.text.x = element_text(angle = 45, hjust = 1, vjust = 1)) +
    facet_wrap(
      vars(location, stat), scales="free_y", ncol = 3,
      labeller=label_wrap_gen(multi_line=FALSE)) +
    theme(strip.background = element_blank(), legend.position = "None") +
    xlab(element_blank())
}
```

## By country

```{r, fig.width=9, fig.height=15}
joined %>%
  plot_prep() %>%
  plot_growth_comps()
```

## China

```{r, fig.width=9, fig.height=15}
joined %>%
  filter(`Country/Region` == "China") %>%
  plot_prep("Province/State") %>%
  plot_growth_comps()
```

## US

```{r, fig.width=9, fig.height=18}
joined %>%
  filter(`Country/Region` == "US") %>%
  plot_prep("Province/State") %>%
  plot_growth_comps()
```

## Canada

```{r, fig.width=9, fig.height=15}
joined %>%
  filter(`Country/Region` == "Canada") %>%
  plot_prep("Province/State") %>%
  plot_growth_comps()
```

## Australia

```{r, fig.width=9, fig.height=15}
joined %>%
  filter(`Country/Region` == "Australia") %>%
  plot_prep("Province/State") %>%
  plot_growth_comps()
```

# Total deaths since first death {.tabset .tabset-fade .tabset-pills}
```{r}
plot_deaths_since_first <- function(df, days_since, init_deaths = 3,
                                    show_top = 1:10) {
  xlab <- paste("Days since deaths =", init_deaths)
  df %>%
    plot_prep("Country/Region", show_top = show_top) %>%
    group_by(location) %>%
    mutate(total_deaths = value[which(date == max(date) &
                                        stat == "deaths")][1]) %>%
    filter(total_deaths > 0) %>%
    mutate(init_death_date = date[which(value >= init_deaths &
                                           stat == "deaths")][1]) %>%
    mutate(
      !!sym(xlab) := date - init_death_date) %>%
    filter(stat == "deaths" & value > 0 &
             between(!!sym(xlab), 0, days_since)) %>%
    ggplot(aes(!!sym(xlab), value, 
               color=location)) + geom_line() +
    theme(strip.background = element_blank(), legend.position = "right",
          legend.title = element_blank()) +
    ylab("Total deaths")
}
```

[Dashboard version](https://robon.shinyapps.io/deathcomp/)

## 10 days since first death
```{r fig.width=8, message=FALSE, warning=FALSE}
joined %>%
  plot_deaths_since_first(10)
```

## 30 days since first death
```{r fig.width=8, message=FALSE, warning=FALSE}
joined %>%
  plot_deaths_since_first(30)
```

## 60 days since first death
```{r fig.width=8, message=FALSE, warning=FALSE}
joined %>%
  plot_deaths_since_first(60)
```

# Double days

## Double days by country/stat {.tabset .tabset-fade .tabset-pills}

### Top 10 countries

```{r fig.height=10, message=FALSE, warning=FALSE}
joined %>%
  plot_prep(dcr_wts = c(100, 0.1, 0), show_top = 1:10) %>%
  group_by(location, stat) %>%
  arrange(date) %>%
  mutate(
    `Days to double` = if_else(
      value < 10, as.difftime("", units="days"),
      date - date[sapply(value, function(x) { which.min(abs(x / 2 - value)) })])
  ) %>%
  ggplot(aes(date, `Days to double`, color=stat)) +
  geom_point(alpha = 0.5) +
  geom_smooth() +
  #facet_wrap(vars(location), ncol = 3) + 
  facet_grid(vars(location), vars(stat), scales="free") + 
  theme(legend.position = "bottom", legend.title=element_blank()) +
  xlab(element_blank())
```

### excl China

```{r fig.height=10, message=FALSE, warning=FALSE}
joined %>%
  filter(`Country/Region` != "China") %>%
  plot_prep(dcr_wts = c(100, 0.1, 0), show_top = 1:10) %>%
  group_by(location, stat) %>%
  arrange(date) %>%
  mutate(
    `Days to double` = if_else(
      value < 10, as.difftime("", units="days"),
      date - date[sapply(value, function(x) { which.min(abs(x / 2 - value)) })])
  ) %>%
  ggplot(aes(date, `Days to double`, color=stat)) +
  geom_point(alpha = 0.5) +
  geom_smooth() +
  #facet_wrap(vars(location), ncol = 3) + 
  facet_grid(vars(location), vars(stat), scales="free") + 
  theme(legend.position = "bottom", legend.title=element_blank()) +
  xlab(element_blank())
```

##  Days to double deaths since nth death {.tabset .tabset-fade .tabset-pills}

h/t: [\@loeserjohn](https://twitter.com/loeserjohn)

[Dashboard version](https://robon.shinyapps.io/days2double/)

### Country/Region level

```{r fig.width=9, message=FALSE, warning=FALSE}
n_deaths <- 10
min_deaths <- n_deaths * 2
top_n_countries <- 1:20
trunc_to_n <- 2
geo_level = "Country/Region"

# TODO(roboton): Refactor into prep and plot functions
joined %>%
  
  # plot prep at country level
  plot_prep(geo_level, show_top = top_n_countries) %>%
  
  # limit to deaths
  filter(stat == "deaths") %>%
  
  # drop countries with less than min_deaths
  group_by(location) %>%
  filter(value[which.max(date)] >= min_deaths) %>%
  ungroup() %>%
  
  # get the first date with more than or equal to n_deaths
  group_by(location) %>%
  mutate(nth_death_date = date[which(value >= n_deaths) - 1][1]) %>%
  mutate(`Days since nth death` = date - nth_death_date) %>%
  
  # Drop days before nth deaths date
  filter(`Days since nth death` > 0) %>%
  
  # Compute Days to Double
  group_by(location) %>%
  
  mutate(
    # set days to double
    double_idx = sapply(value, FUN=function(x) { max(which(value <= x/2)) }),
    `Days to double` = date - date[double_idx]
  ) %>%
  filter(!is.na(`Days to double`)) %>%
  ## plot
  # truncate to second longest time series
  group_by(location) %>%
  mutate(max_days_since_nth_death = max(`Days since nth death`)) %>%
  ungroup() %>%
  mutate(max_days_rank = dense_rank(desc(max_days_since_nth_death))) %>%
  filter(`Days since nth death` <= max(`Days since nth death`[max_days_rank == trunc_to_n])) %>%
  group_by(location) %>%
  mutate(time_points = n()) %>%
  ungroup() %>%
  filter(time_points > 2) %>%
  ggplot(aes(`Days since nth death`, `Days to double`, color=location)) +
  geom_line(stat = "smooth", method = "auto", se = F) +
  geom_point(alpha = 0.2, size = 1) +
  xlab(paste("days since deaths =", n_deaths)) -> p
  ggplotly(p)
```

### Long view

```{r fig.width = 9, warning = FALSE, message= FALSE}
n_deaths <- 10
min_deaths <- n_deaths * 2
top_n_countries <- 1:15
trunc_to_n <- 1
geo_level = "Country/Region"

# TODO(roboton): Refactor into prep and plot functions
joined %>%
  #filter(!is.na(`Province/State`) & `Province/State` != `Country/Region`) %>%
   
  # plot prep at country level
  plot_prep(geo_level, show_top = top_n_countries) %>%
  
  # limit to deaths
  filter(stat == "deaths") %>%
  
  # drop countries with less than min_deaths
  group_by(location) %>%
  filter(value[which.max(date)] >= min_deaths) %>%
  ungroup() %>%
  
  # get the first date with more than or equal to n_deaths
  group_by(location) %>%
  mutate(nth_death_date = date[which(value >= n_deaths) - 1][1]) %>%
  mutate(`Days since nth death` = date - nth_death_date) %>%
  
  # Drop days before nth deaths date
  filter(`Days since nth death` > 0) %>%
  
  # Compute Days to Double
  group_by(location) %>%
  
  mutate(
    # set days to double
    double_idx = sapply(value, FUN = function(x) { max(which(value <= x/2)) }),
    `Days to double` = date - date[double_idx]
  ) %>%
  filter(!is.na(`Days to double`)) %>%
  ## plot
  # truncate to second longest time series
  group_by(location) %>%
  mutate(max_days_since_nth_death = max(`Days since nth death`)) %>%
  ungroup() %>%
  mutate(max_days_rank = dense_rank(desc(max_days_since_nth_death))) %>%
  filter(`Days since nth death` <= max(`Days since nth death`[max_days_rank == trunc_to_n])) %>%
  group_by(location) %>%
  mutate(time_points = n()) %>%
  ungroup() %>%
  filter(time_points > 2) %>%
  ggplot(aes(`Days since nth death`, `Days to double`,
             color = location)) +
  geom_line(stat = "smooth", method = "auto", se = F) +
  geom_point(alpha = 0.2, size = 1) +
  xlab(paste("days since deaths >=", n_deaths)) -> p
  ggplotly(p) 
```

# Prototype

```{r}
min_total <- 10
min_stat <-  "deaths"
geo_level <- "Country/Region"
max_days_since <- 20
min_total <- 50


joined %>%
  rename(total = value) %>%
  mutate(location = !!sym(geo_level)) %>%
  # aggregate
  group_by(location, stat, date) %>%
  summarise(total = sum(total)) %>%
  # add cfr
  bind_rows(
    (.) %>%
      select(location, stat, date, total) %>%
      spread(stat, total) %>%
      mutate(cfr = deaths/confirmed) %>%
      gather(stat, total, confirmed, deaths, recovered, cfr) %>%
      filter(stat == "cfr")) %>%
  # get max_total and first_date per location/stat
  group_by(location, stat) %>%
  mutate(max_total = max(total),
         first_date = min(date[total >= min_total])) %>%
  group_by(location) %>%
  # drop earlier dates
  filter(date >= first_date[stat == min_stat]) %>%
  # recenter dates
  mutate(days_since = date - first_date[stat == min_stat]) %>%
  ungroup() %>%
  # calc double_days
  group_by(location, stat) %>%
  mutate(
    double_days = date -
      date[sapply(
        total, FUN = function(x) { max(which(total <= x/2)) })]) %>%
  ungroup() %>%
  gather(value_type, value, total, double_days) %>%
  ## plotting
  # truncate days_since
  filter(days_since <= max_days_since) %>%
  # dropping small locations
  group_by(location) %>%
  filter(max_total[stat == min_stat][1] >= min_total) %>%
  ungroup() %>%
  ggplot(aes(days_since, value, color = location)) +
  geom_line() +
  facet_wrap(vars(stat, value_type), scales = "free", ncol=2,
             labeller = label_wrap_gen(multi_line = FALSE))
```

